Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
Year: 2026
Type: conference-paper
Abstract: Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions.However, it remains unclear whether these simulations reflect precise user-specific beliefs or whether they are highly sensitive to semantically independent changes in conversational contexts.In this work, we study counterfactual context revision as a framework for auditing LLM-based stance simulation.Given an original online conversation, we first infer a target user's stance toward a specific topic.We then apply controlled revision strategies to the conversational context and simulate the user's stance again under the revised context.We compare text-only revision strategies with a multimodal one that incorporates meme-based context and evaluate two main effectiveness metrics, i.e., average directional stance shift and stance transition rate.The results reveal effective and robust stance transitions in both textonly and multimodal strategies across different polarization-preference mechanisms.Our study contributes an evaluation framework for understanding the context sensitivity of LLMbased stance simulation (https://github. com/STARResearchLab/StanceShift).More broadly, it highlights both the promise and risk of using LLMs as proxies for social media users in social simulation.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7163674635